Method for estimating a correlation matrix of interfering signals received through a sensor array

ABSTRACT

Process for estimating the correlation matrix of signals of unknown characteristics in an array of N sensor. The process determines the correlation matrix {circumflex over (R)}s of the signals of known characteristics, estimates the correlation matrix {circumflex over (R)}x of the sensor signals, forms a matrix A equal to A={circumflex over (R)}s −1/2 {circumflex over (R)}x {circumflex over (R)}s −1/2  and decomposes the matrix A into eigenelements, constructs a matrix B based on the eigenelements of the matrix A, and determines the estimated correlation matrix {circumflex over (R)}b of the interfering signals based on the estimated matrix {circumflex over (R)}s and of the matrix B, such that {circumflex over (R)}b={circumflex over (R)}s 1/2  B {circumflex over (R)}s 1/2 . Such a process may find application to antenna processing.

The present invention relates to a process for estimating a correlation matrix of signals with unknown characteristics, which signals are received by an array comprising a specified number N of sensors, the sensors receiving signals composed in particular of signals having known characteristics and of signals of unknown characteristics such as noise and/or jammers.

The invention finds its application in various fields, in particular the following:

-   -   the goniometry of various sources arriving at the array, that is         to say the determination of their directions of arrival,     -   spatial filtering so as to protect one or more links from         interfering sources,     -   any other device in which it is desirable to estimate the         correlation matrix of useful signals and that of the interfering         signals.

In numerous applications in particular in the field of telecommunications, the use of antenna processing methods is turning out to be especially beneficial.

Among the signals arriving at the array of sensors which constitute the antenna, some are of known characteristics, the useful signals in telecommunications for example, others exhibit unknown characteristics, such as interference. A large number of these methods are based on the estimation of the correlation matrix of the signals received by the sensors.

In the subsequent description various parameters and notations are used, in particular the following:

-   -   N, the number of sensors of an array,     -   x(n), the observation vector received by the array of N sensors         after digitization, or sensor signals,     -   x_(u)(n), the component of the observation vector corresponding         to the signals referred to as useful signals, whose         characteristics are known to the processing,     -   M, the number of useful signals,     -   X_(b)(n), the component of the observation vector corresponding         to the signals referred to as interfering signals (noise and/or         jammers), whose characteristics are unknown to the processing,     -   P, the number of interfering signals,     -   Rx=E(x(n) x(n)^(†)), the theoretical correlation matrix of the         sensor signals,     -   Rs, the theoretical correlation matrix of the signal vector         x_(u)(n),     -   Rb, the theoretical correlation matrix of the signal vector         x_(b)(n),     -   ^(‘†’), represents the transposition-conjugation operation.

Assuming that the useful signals are decorrelated from the interfering signals, relation (1) linking the various theoretical correlation matrices defined hereinabove can be expressed in the following manner: Rx=E(x(n)x(n)^(†))=E([x _(u)(n)+x _(b)(n)][x _(u)(n)+x _(b)(n)]^(†)) Rx=E(x _(u)(n)x _(u)(n)^(†))+E(x _(b)(n)x _(b)(n)^(†))+2Re{E(x _(u)(n)x _(b)(n)^(†))} Rx=Rs+Rb  (1)

Knowing the correlation matrix of the useful signals Rs and of the matrix Rx, the matrix Rb can be obtained simply on the basis of relation (1): Rb=Rx−Rs.  (1′)

Applied to the estimates of these correlation matrices, relation (1′) does not yield good results, as is demonstrated for example in the book “Introduction to adaptive arrays” written by R. A. Monzingo, W. Miller, and published by John Wiley and Sons, New York, 1980. Specifically, the estimate of the correlation matrix Rx also comprises terms relating to the correlation between the useful signals and the interfering signals E(x_(u)(n) x_(b)(n)^(†)), which are only asymptotically zero.

The subject of the invention relates to a process making it possible to estimate in particular the correlation matrix of the interfering signal on the basis of the correlation matrix of the signals received on an array comprising a number N of sensors and of an estimate of the correlation matrix of the useful signals.

The invention relates to a process for estimating the correlation matrix of signals of unknown characteristics in an array comprising a number N of sensors, said sensors receiving at least the signals Sb of unknown characteristics and signals Su of known characteristics, the Su and Sb together forming the sensor signals Sx. It is characterized in that it comprises at least the following steps:

-   -   a) determining the correlation matrix {circumflex over (R)}s or         Rs, α of the signals Su of known characteristics,     -   b) estimating the correlation matrix {circumflex over (R)}x of         the sensor signals Sx,     -   c) forming a matrix A equal to A={circumflex over         (R)}s^(−1/2){circumflex over (R)}x {circumflex over (R)}s^(−1/2)     -   d) decomposing the matrix A into eigenelements so as to obtain         its eigenvalues λ_(i) and its eigenvectors u₁, with         λ₁≦λ₂≦ . . . ≦λ_(N)     -   e) constructing a matrix B having eigenvectors v₁ substantially         equal to the eigenvectors u_(i) of the matrix A, and eigenvalues         γ_(i) equal to the eigenvalues λ₁ to within a constant β, the         value of the constant β being determined on the basis of the N-P         smallest eigenvalues of the matrix A, P being the number of         jammers or interfering signals, with         λ₁≦β≦λ_(N)     -   f) determining the estimated correlation matrix {circumflex over         (R)}b of the interfering signals Sb on the basis of the         estimated matrix {circumflex over (R)}s and of the matrix B,         such that {circumflex over (R)}b={circumflex over (R)}s^(1/2) B         {circumflex over (R)}s^(1/2).

For a matrix B of rank N-P, the value of the constant β is for example the smallest of the eigenvalues λi or else it is determined on the basis of the N-P smallest of the eigengvalues λi by taking the average value.

The process according to the invention applies also to antenna processing.

The invention also relates to a device for estimating the correlation matrix of signals of unknown characteristics in an array comprising a number N of sensors, said sensors receiving at least the signals Sb of unknown characteristics and signals Su of known characteristics, the Su and Sb together forming the sensor signals Sx. It is characterized in that it comprises means suitable for implementing the process according to one of the characteristics described herein above.

The invention exhibits in particular the following advantages:

-   -   in applications involving goniometry, it makes it possible to         reduce the number of sensors used, the goniometry being         performed solely on certain signals, and to improve the accuracy         of goniometry of the selected sources,     -   in SLC applications, the abbreviation standing for the         expression “side lobe canceller”, the sought-after power         minimization can be carried out solely on the interfering         signals while protecting the useful signals,     -   in SMF applications, the abbreviation standing for (spatial         matched filter), the process according to the invention makes it         possible to increase the speed of convergence of the processing         and the robustness with respect to errors.

Other characteristics and advantages of the invention will become apparent with the aid of the description which follows given, by way of wholly nonlimiting illustration, in conjunction with the appended drawings which represent:

FIG. 1 diagrammatically shows the various signals arriving at an array comprising a number N of sensors,

FIG. 2 represents an algorithm comprising the steps of the process according to the invention,

FIG. 3 diagrammatically shows an SLC device, and

FIG. 4 represents an application of the process according to the invention to spatial matched filtering.

The process according to the invention uses in particular the estimates of the correlation matrix of the useful signals and of the correlation matrix of the sensor signals so as to deduce therefrom the estimate of the correlation matrix of the interfering signals.

FIG. 1 diagrammatically shows an antenna comprising an array of N sensors Ci. The sensors Ci receiving all the useful signals, noise, jammers, are each linked with a receiver, the N receivers Di together constituting a multichannel receiver known to the person skilled in the art. The multichannel receiver is itself linked to a device 1 for digitizing and processing the signals by carrying out the steps of the process according to the invention. To do this, the device comprises for example a microprocessor 2 or a computer suitable for implementing the steps.

The process according to the invention relies on concepts recalled by using the theoretical matrices hereinbelow, on the basis of the relation Rx=Rs+Rb  (1) Rs stands for the matrix Rs which may be known or more generally Rs,α the matrix Rs known to within the coefficient α; Rs,α=αRs.

The idea consists in

a) “whitening” the observation matrix Rs as follows:

1—if the matrix Rs is invertible, the process consists in right and left multiplying the two terms of equation (1) by (Rs,α)^(−1/2), the following expression (2) is obtained: (Rs,α)^(−1/2) Rx(Rs,α)^(−1/2)=(Rs,α)^(−1/2) Rs(Rs,α)^(−1/2)+(Rs,α)^(−1/2) Rb(Rs,α)^(−1/2) (Rs,α)^(−1/2) Rx(Rs,α)^(−1/2)=α⁻¹ Id+(Rs,α)^(−1/2) Rb(Rs,α)^(−1/2)  (2) or else A=α⁻¹ Id+B with Id the identity matrix

2—if the matrix Rs is not invertible, this being the case particularly if the number M of useful signals is less than the number N of sensors of the array N. The matrix is rendered invertible by adding a term δ Id, with δ<σ², where σ² is the background noise power. The matrix to be considered in the subsequent steps of the process then corresponds to Rs,α=α Rs+δ Id.

The correlation matrix Rb calculated subsequently will comprise the entire contribution from the interfering signals, and a reduced contribution from the background noise (of power σ²−δ), this amounting to artificially increasing the INR (Interference to Noise Ratio) of the interfering signals in the estimated correlation matrix.

As mentioned above, expression (2) can be of the form A=α⁻¹ Id+B b) decomposing whitened matrix A obtained in the course of step a) into eigenelements $\begin{matrix} {A = {{R\quad s^{{- 1}/2}R\quad x\quad R\quad s^{{- 1}/2}} = {\sum\limits_{i - 1}^{N}{\lambda_{i}u_{i}u_{i}^{t}}}}} & (3) \end{matrix}$ where: λ_(i) and u_(i) respectively represent the eigenvalues and the eigenvectors of the matrix A.

At the end of step b) the eigenvalues λ_(i) and the eigenvectors u_(i) of the matrix A are known, with λ₁≦λ₂≦ . . . ≦λ_(N) c) constructing a matrix B on the basis of the eigenvalues and of the eigenvectors obtained in step b).

In the case where the number of interfering signals is strictly less than the number N of sensors, and in the absence of background noise, the matrix B=(Rs,α)^(−1/2) Rb (Rs,α)^(−1/2) is then of rank N-P, (N being the number of sensors and P the number of interfering signals) and according to equation (2) the matrix A possesses P eigenvalues greater than α⁻¹ and N-P identical eigenvalues such that: λ₁=λ₂= . . . =λ_(N-P)=α⁻¹.

Under these conditions, the matrix B computed has eigenelements:

-   -   eigenvectors vi which are substantially identical to the         eigenvectors ui of the matrix A, and     -   eigenvalues γi equal to the eigenvalues λ₁—λ₁

The matrix B can for example be expressed through the relation $\begin{matrix} {B = {\sum\limits_{i = 1}^{N}{\left( {\lambda_{i} - \lambda_{1}} \right)u_{i}u_{i}^{t}}}} & (4) \end{matrix}$ with γi=(λ_(i)—λ₁) d) knowing B and the correlation matrix Rs, it is possible to obtain the correlation matrix of the interfering signals Rb=Rs^(1/2) B Rs^(1/2)  (5)

Relations (2) to (5) above, written out on the basis of the theoretical correlation matrices, serve as a basis for the steps of the process according to the invention which use the estimates of the matrices.

The steps set forth hereinabove using the theoretical correlation matrices are applied to the matrix estimates.

This involves the knowledge of the estimates of the correlation matrices of the useful signals and of the sensor signals so as to determine the estimate of the matrix of the interfering signals.

To do this, the information available regarding the useful signals must make it possible to construct a good enough estimation of the correlation matrix Rs, to within a multiplicative constant. This estimation can be obtained in several ways described hereinbelow.

The number P of interfering signals is preferably less than the number N of sensors constituting the reception antenna. If this latter condition is not satisfied, a bias will be obtained in the estimation of the matrix Rb.

The process implements several steps mentioned in the algorithm of FIG. 2.

A) Determining the Whitened Matrix A Through the Following Steps

1st step—10—estimating the correlation matrix {circumflex over (R)}s on the basis of the information regarding the useful signals.

For example, if the processing knows the direction vectors (which may be determined on the basis of the directions of arrival) and the powers of the useful signals, the estimated correlation matrix {circumflex over (R)}s may be written: $\begin{matrix} {\hat{R\quad s} = {\sum\limits_{m = 1}^{M}{\pi_{m}s_{m}s_{m\quad t}}}} & (6) \end{matrix}$

-   -   with π_(m)=the power     -   and s_(m)=direction vector

If the correlation matrix thus formed is not invertible, the matrix is rendered invertible as set forth above by adding a term δ Id: $\begin{matrix} {\hat{R\quad s} = {{\sum\limits_{m = 1}^{M}{\pi_{m}s_{m}s_{m\quad t}}} + {\delta\quad I\quad d}}} & (7) \end{matrix}$ as the case may be, expression (6) or expression (7) is used subsequently in the process.

2nd step—20—estimation of the correlation matrix of the sensor signals received over K samples, on the basis for example of the unbiased conventional estimator: $\begin{matrix} {\hat{R\quad x} = {\frac{1}{K}{\sum\limits_{k = 1}^{K}{{x(k)}{x(k)}^{t}}}}} & (8) \end{matrix}$

The integration of the correlation matrix {circumflex over (R)}x must be performed over a sufficient number of samples K so as to be able to neglect the correlation terms correlating the useful signals and the interfering signals.

3rd step—30—calculation of the matrix A={circumflex over (R)}s^(−1/2){circumflex over (R)}x{circumflex over (R)}s^(−1/2) and decomposition of the matrix A into eigenelements $\begin{matrix} {A = {\sum\limits_{i = 1}^{N}{\lambda_{i}u_{i}u_{i}^{t}}}} & (9) \end{matrix}$ with ⁻R_(s) ^(−1/2) inverse square root of R_(s)

The calculation and decomposition are described for example in the book entitled “Numerical Recipes in C” written by W. H. PRESS, B. R. FLANNERY, S. A. TEUKOLSY, W. T. VETTERLING, 1992 (Publisher: Cambridge University).

On completion of this 3rd step the eigenvectors u_(i) and the eigenvalues λ₁ of the matrix A are known.

B) Determining a Matrix B and the Estimate of the Matrix of the Interfering Signals

4th step—40—formation of the matrix B on the basis of the elements of A by determining its eigenelements: eigenvectors and eigenvalues

-   -   the eigenvectors v₁ are substantially equal or equal to the         eigenvectors u₁ obtained by decomposition of the matrix A,     -   the eigenvalues γ₁ are substantially equal or equal to the         eigenvalues λ_(i) obtained by decomposition of the matrix A and         corrected by a value or constant β which is chosen on the basis         of the N-P smallest eigenvalues of the matrix A or equal to the         smallest eigenvalue of the matrix A.

The matrix B then takes the following expression: $\begin{matrix} {\sum\limits_{i = 1}^{N}{\left( {\lambda_{i} - \beta} \right)u_{i}u_{i}^{t}}} & (10) \end{matrix}$

Advantageously, the value of the constant β is the smallest of the eigenvalues λ₁.

According to another embodiment, the value of the constant β is determined by averaging the N-P eigenvalues of the matrix A.

5th step—50—determining the correlation matrix of the interfering signal vectors

On the basis of B and of the estimated value of the matrix {circumflex over (R)}s, the process makes it possible to obtain the estimated value of the correlation matrix {circumflex over (R)}b corresponding to the interfering signals. {circumflex over (R)}b={circumflex over (R)}s^(1/2) B {circumflex over (R)}s^(1/2)  (11)

Knowledge of the estimated value of the correlation matrix of the interfering signals can be used in various processings of signals, some examples of which are given hereinbelow by way of wholly nonlimiting illustration.

The process according to the invention is applied in various types of applications, some of which are given by way of wholly nonlimiting illustration.

Application to Goniometry

Several methods of goniometry are based on utilizing the correlation matrix of the signals received on an array of sensors. Some of these methods, the so-called high-resolution methods such as the MUSIC method described for example in reference [1] or the ESPRIT method described in reference [2] for example make it possible to effect the goniometry of several sources arriving simultaneously on an array of sensors.

-   [1] Article entitled “Multiple Emitter Location and Signal Parameter     Estimation” from the IEEE Trans. Ant. Prop. journal, vol. AP-34, No.     3, pp 276-280, March 1986 written by M. R. D. Schmidt. -   [2] Article entitled “Estimation of Signal Parameters via Rotational     Invariance Techniques” from the IEEE Transaction ASSP journal, vol.     ASSP-37, pp 984-995, July 1989 written by M M. Roy, T. Kailath.

However, these methods possess certain limitations: the use of an array of N sensors thus typically makes it possible to perform the goniometry of N/2 sources.

When the array receives useful signals and interfering signals, it is possible to separate them, by implementing the process steps described hereinabove. After having separated the interfering signals from the useful signals, the goniometry is for example performed according to the methods known to the person skilled in the art, by implementing solely the estimated correlation matrix {circumflex over (R)}b. The goniometry determines the directions of arrival of the P interfering signals, and not the directions of arrival of M+P sources, thereby improving its performance. It is carried out as if the useful signals were absent.

The application of the process according to the invention requires a lesser number of sensors than that customarily used in the prior art.

Application to Spatial Filtering

SLC

The basic diagram of the SLC is recalled in FIG. 3. The SLC is used to protect the useful signals arriving from a given direction in relation to interfering signals.

It is described for example in the following references [3] and [4]:

-   [3] Article entitled “Adaptive Arrays” from the IEEE Trans. Ant.     Prop. journal, vol. AP 24, No.5, pp 585-598, September 1976, written     by S. P. Applebaum. -   [4] Article entitled “Explorations in fixed and adaptive resolution     at GE and SURC”, from the IEEE Trans. Ant. Prop. journal, vol. AP     24, No.5, pp 585-598, September 1976, written by P. W. Howells.

It is based on the use of a directional main sensor 60, pointing in the direction of the useful signals, and of L auxiliary sensors 61 making it possible to eliminate the influence of the interfering signals present in the main channel. The L auxiliary sensors and the main sensor constitute the array of N sensors.

The weightings w_(a) of the SLC are calculated in such a way as to minimize the total power of the output signal y_(p)(†)−w_(a) ^(†) z(†). They are expressed on the basis of Rz, the correlation matrix of the signals of the auxiliary channels and of r_(Zy), the cross-correlation vector between z(†) and y_(p)(†): w_(a)=Rz⁻¹r_(Zy)  (12) with Rz=E(z(†), z(†)^(†)) and r_(Zy=)E(z(†), y_(p)(†)^(†)) where z(†) is the output signal of the auxiliary channels and y_(p)(†) the output signal of the main channel.

The SLC must be employed with care, so as to avoid the risk of eliminating the useful signals. The lower the power of the jammers relative to the useful ones, or the greater the number of auxiliary channels than the number of jammers, the larger is this risk. The SLC must be properly conditioned. Thus, the auxiliary channels must be as undirectional as possible in the direction of the useful signals and as directional as possible in the direction of the jammers.

In order to avoid the risk of eliminating the useful signals, several methods of robustification of the SLC exist in the literature:

-   -   addition of fictitious noise in such as way as to “mask” the         presence of the useful signals,     -   norm constraint on the weightings w_(a):∥ w_(a):∥<Max, . . . .

The process according to the invention consists in implementing the SLC as if the useful signals were absent, by estimating the correlations of expression (12) corresponding to the interfering signals. The risk of their elimination is thus removed and the algorithm optimizes the elimination of the interfering signals and not of the useful signals.

To do this, the correlation matrix {circumflex over (R)}b is estimated by implementing the steps of the process according to the invention and by putting x(†)=[z(†)^(T) y_(p)(†)]^(T). Formula (12) then makes it possible to obtain the weightings w_(a), by regarding the matrix Rz (respectively the vector r_(Zy)) as corresponding to the first L rows and to the first L columns (respectively to the first L rows of the last column) of the estimated matrix {circumflex over (R)}b.

Spatial Matched Filter—FIG. 4

In order to protect a useful signal whose direction of arrival is known, it is possible to implement the spatial matched filter based on direction of arrival.

The algorithm used consists in minimizing the total output power of the antenna while maintaining a unit gain in the direction of the useful signal. The following notation is used:

-   -   w, the weighting vector corresponding to the spatial filter,     -   s, the direction vector of the useful signal, which is obtained         for example on the basis of the knowledge of the direction of         the useful signal, known or estimated by goniometry for example,     -   y(†)=w^(†) x(†) the output of the spatial filter.

The vector w may be written: w=a Rx⁻¹s, where a is a near multiplicative constant.

The obtaining of the vector w is for example described in reference [5] entitled “Système mixte de goniométrie à haute résolution et de réjection adaptative de brouillerus dans la gamme HF” [Hybrid system for high-resolution goniometry and adaptive rejection of jammers in the HF range], written by F. Pipon, P. Chevalier, G. Multedo and published in Proc. GRETSI, pp 685-688, September 1991.

The spatial filter leads to the maximization of the SNIR: ratio of the power of the signal arriving from the direction corresponding to the direction vector s (useful signal) to the sum of the power of the background noise and of the power of the signals coming from the other directions (interfering signals).

In practice, w is estimated using the estimated correlation matrix {circumflex over (R)}x, and a vector ŝ close to the true direction vector s.

This technique requires that the direction vector s be known perfectly. Specifically, when ŝ does not correspond to the direction vector s, the algorithm is at risk of eliminating the useful signal.

The process according to the invention makes it possible to circumvent this risk and to improve the performance of the spatial matched filter.

The spatial filter w is calculated on the basis of the estimated matrix {circumflex over (R)}b, determined by implementing the process according to the invention, calculated according to the invention, and of ŝ. The spatial filter w is then equal to: a {circumflex over (R)}b⁻¹s.

The use of this method also advantageously makes it possible to improve the speed of convergence of the algorithm for calculating w.

Without departing from the scope of the invention, the process is applied in particular in the following fields:

-   -   irrespective of the type of source: narrowband, broadband, or         multipath,     -   space telecommunications, for example for protecting a theater         of operations with regard to interference,     -   passive listening, for example after a goniometry step and         intensimetry of the transmitters received,     -   GE: angular locating of interference in the presence of known         signals,     -   radar, acoustics, radio communications. 

1. A process for estimating a correlation matrix of signals of unknown characteristics, which are received by an array comprising a number N of sensors, to process signals received by said array, said sensors receiving at least first signals of unknown characteristics and second signals of known characteristics, the first and second signals together forming sensor signals, the process comprising: a) determining a correlation matrix {circumflex over (R)}s or Rs,α of the signals of known characteristics; b) estimating a correlation matrix {circumflex over (R)}x of the sensor signals; c) forming a matrix A equal to A={circumflex over (R)}s^(−1/2){circumflex over (R)}x {circumflex over (R)}s^(−1/2); d) decomposing the matrix A into eigenelements to obtain its eigenvalues λ_(i) and its eigenvectors u_(i), with λ₁≦λ₂≦ . . . ≦λ_(N) e) constructing a matrix B having eigenvectors v1 substantially equal to the eigenvectors u_(i) of the matrix A, and eigenvalues γ_(i) equal to the eigenvalues λ₁ to within a constant, a value of a constant β being determined based on N-P smallest eigenvalues of the matrix A, P being a number of jammers or interfering signals, with λ₁≦β≦λ_(N-P); and f) determining an estimated correlation matrix {circumflex over (R)}b of interfering signals based on the estimated matrix {circumflex over (R)}s and the matrix B, such that {circumflex over (R)}b={circumflex over (R)}s^(1/2) B {circumflex over (R)}s^(1/2).
 2. The process as claimed in claim 1, wherein β corresponds to a smallest of the eigenvalues λ_(i).
 3. The process as claimed in claim 1, wherein β is determined based on the N-P smallest of the eigenvalues λ_(i) by taking an average value.
 4. The process as claimed in claim 1, wherein the correlation matrix {circumflex over (R)}s determined in a) is determined based on information regarding a power and directions of arrival of useful signals according to expression: $\hat{R} = {\sum\limits_{m = 1}^{M}\quad{\pi_{m}\quad s_{m}\quad s_{m}^{t}}}$ with π_(m)=the power and s_(m)=direction vector.
 5. The process as claimed in claim 1, wherein the matrix Rs or Rs,α, is rendered invertible by adding a term δ Id, with δ<σ² where σ² is a background noise power.
 6. The process as claimed in claim 1, further comprising performing goniometry on the interfering signals by using the estimated matrix {circumflex over (R)}b obtained in f).
 7. The process as claimed in claim 1, further comprising determining weightings w_(a) in a device of SLC type where the N sensors comprise L auxiliary sensors and a main sensor, by determining a matrix Rz corresponding to first L rows and first L columns of the estimated matrix {circumflex over (R)}b and a vector R_(Zy) corresponding to the first L rows of a last column of the estimated matrix {circumflex over (R)}b and by using the expression w_(a)=Rz⁻¹ r_(Zy) with Rz=E(z(†), z(†)^(†)) and r_(Zy)=E(z(†), y_(p)(†)^(†)).
 8. The process as claimed in claim 1, further comprising determining a spatial filter w based on the estimated matrix {circumflex over (R)}b with w=a {circumflex over (R)}b⁻¹s.
 9. The application of the process as claimed in claim 1 to antenna processing.
 10. A device for estimating a correlation matrix of signals of unknown characteristics, which are received by an array comprising a number N of sensors, to process the signals received by said array, said sensors receiving at least first signals of unknown characteristics and second signals of known characteristics, the first and second signals together forming sensor signals, the device comprising means for implementing the process as claimed in claim
 1. 